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Erschienen in: International Journal of Computer Assisted Radiology and Surgery 7/2017

30.03.2017 | Original Article

SLIDE: automatic spine level identification system using a deep convolutional neural network

verfasst von: Jorden Hetherington, Victoria Lessoway, Vit Gunka, Purang Abolmaesumi, Robert Rohling

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 7/2017

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Abstract

Purpose

Percutaneous spinal needle insertion procedures often require proper identification of the vertebral level to effectively and safely deliver analgesic agents. The current clinical method involves “blind” identification of the vertebral level through manual palpation of the spine, which has only 30% reported accuracy. Therefore, there is a need for better anatomical identification prior to needle insertion.

Methods

A real-time system was developed to identify the vertebral level from a sequence of ultrasound images, following a clinical imaging protocol. The system uses a deep convolutional neural network (CNN) to classify transverse images of the lower spine. Several existing CNN architectures were implemented, utilizing transfer learning, and compared for adequacy in a real-time system. In the system, the CNN output is processed, using a novel state machine, to automatically identify vertebral levels as the transducer moves up the spine. Additionally, a graphical display was developed and integrated within 3D Slicer. Finally, an augmented reality display, projecting the level onto the patient’s back, was also designed. A small feasibility study \((n=20)\) evaluated performance.

Results

The proposed CNN successfully discriminates ultrasound images of the sacrum, intervertebral gaps, and vertebral bones, achieving 88% 20-fold cross-validation accuracy. Seventeen of 20 test ultrasound scans had successful identification of all vertebral levels, processed at real-time speed (40 frames/s).

Conclusion

A machine learning system is presented that successfully identifies lumbar vertebral levels. The small study on human subjects demonstrated real-time performance. A projection-based augmented reality display was used to show the vertebral level directly on the subject adjacent to the puncture site.
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Metadaten
Titel
SLIDE: automatic spine level identification system using a deep convolutional neural network
verfasst von
Jorden Hetherington
Victoria Lessoway
Vit Gunka
Purang Abolmaesumi
Robert Rohling
Publikationsdatum
30.03.2017
Verlag
Springer International Publishing
Erschienen in
International Journal of Computer Assisted Radiology and Surgery / Ausgabe 7/2017
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-017-1575-8

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